This paper outlines research and development towards an integrated Earth sensing sensorweb system for improved crop and rangeland yield predictions. The paper introduces the concepts of integrated Earth sensing and in situ sensorwebs and describes the key aspects and innovations of an intelligent sensorweb for integrated Earth sensing ( ISIES). ISIES incorporates a sensorweb that provides automatic and continuous in situ measurements and advanced crop growth models and leading-edge sensorweb-enabled OpenGIS-compliant Web services. A key component of each in situ sensorweb node is SmartCore, a compact device developed to control sensor data traffic autonomously and to communicate wirelessly in two-way mode with the ISIES central server. The system server automatically integrates the in situ sensorweb data with remote sensing data and crop models to provide maps of leaf area index and soil moisture and biomass and improved predictions of crop and rangeland yield.

Integration of meteorological and remote sensing data in crop growth models offers a potentially powerful tool for yield monitoring. Leaf area index (LAI) is a key variable in crop growth models. The derivation of reliable LAI maps from satellite imagery would provide a means of spatially extrapolating these models. As part of a two-year project to develop an intelligent sensorweb system for yield prediction in agricultural crops and rangeland, the ability to obtain reliable LAI estimates from Compact High Resolution Imaging Spectrometer (CHRIS) data was investigated. Throughout the 2004 and 2005 growing season, data from the CHRIS sensor were acquired over two contrasting sites in Alberta, a wheat crop and rangeland. The modified triangular vegetation index (MTVI2) was used to derive LAI values which were compared to ground- based LAI data collected weekly or tri-weekly in wheat and monthly on the rangeland. A strong relationship was observed between ground-based and remote sensing derived LAI in the case of wheat (r=0.91-0.93). In, the rangeland, where senescent vegetation is a confounding factor, LAI was consistently overestimated using the CHRIS imagery.

This paper outlines research and development efforts towards an Intelligent Sensorweb for Integrated Earth Sensing (ISIES). After introducing the integrated Earth sensing concept and summarizing some prototype in-situ sensorweb demonstration projects, the paper goes on to describe the key aspects and early results of the ISIES project. The objective is to develop an intelligent sensorweb system that integrates in-situ sensors with remote sensing and auxiliary data to provide improved predictions of crop and rangeland yield. The ISIES topics include test sites, sensorweb, data collection, plant models, host server, viewer and server software, and products.